A Simple Car Wash Dilemma | 53 AI Models Tested on Logical Reasoning

A recent examination of AI models revealed surprising results regarding simple logic. Fifty-three models were prompted with a straightforward query: Should one walk or drive to a car wash just 50 meters away? The results highlight a growing divide in AI reasoning capabilities.

The Logic Test in Action

This viral test, designed to gauge reasoning, showed that human intuition can easily decipher the answer, while many AI models struggle. The prompt lacks context, making it a decent benchmark for assessing the decision-making capabilities of AI.

Out of 530 API calls across 53 models, only five consistently answered correctly. Notably, Claude Opus 4.6 and Gemini 2.0 stood out, achieving perfect scores in all trials, while others like GPT-5 faltered, only making the right call 70% of the time.

Surprising Insights from the Test

Participants shared significant skepticism regarding model capabilities, with comments indicating that the question should not have posed any difficulty. One said, "I don’t think this qualifies as a riddle; there is no hidden trick." The consensus suggests that this test speaks volumes about AI's current limitations.

Interestingly, some models provided convoluted justifications. For instance, Perplexity's Sonar suggested that walking might be more polluting than driving due to calorie expenditure. While this reasoning might sound plausible in theory, it misses the question's simplicity.

Reactions and Sentiments

Feedback from the discussions reflects a mixed sentiment, pointing toward disbelief in the models' failures:

• Critics challenged the validity of AI interpreters, asserting that

  ">AI does not demonstrate true intelligence if it can't answer basic questions."

• Some users were amused, remarking on the differing performance levels among models, such as Gemini 2 Flash Lite, which aced it perfectly, contrasting with Gemini 2.5 Pro's 40% success rate.

• General consensus was that more models should perform better; many echoed, "Wake up, babe, new test just dropped."

Key Takeaways

• △ Only 5 of 53 models answered correctly every time

• ▽ Mixed feelings as participants express doubt about AI reasoning

• ※ "This isn’t a riddle; it’s a logical question" - user comment

This testing phase sparks a larger conversation about AI's current capabilities and its role in decision-making contexts—an indication that while developers strive for advancements, many models still fall short in fundamental reasoning tasks.

What Lies Ahead for AI Logic

Experts estimate that within the next few years, there’s a strong chance that AI models will undergo significant enhancements in logical reasoning abilities. As developers focus on refining algorithms, increasing the data sets used for training, and improving interpretative frameworks, we could see a jump in correct responses from models—potentially to over 50% accuracy by 2028. However, there remains skepticism about whether AI can achieve true human-like reasoning. A continuous feedback loop from public testing and forums might accelerate advancements as models adapt and learn from their failures.

Echoes of History in the AI Debate

This scenario bears a curious resemblance to early attempts in aviation—specifically, the Wright Brothers’ flights versus the skepticism faced by their contemporaries. Just as many doubted human flight was feasible, today’s debates about AI reasoning capabilities reflect a similar split between innovators' visions and public disbelief. As the early aviators innovated and refined their designs, the AI field may follow suit, transforming initial failures into definitive breakthroughs. Much like the aircraft took to the skies, AI might soon soar past current constraints, proving the cynics wrong and redefining our understanding of intelligent machines.